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/aosp_15_r20/external/pytorch/test/torch_np/numpy_tests/lib/
H A Dtest_type_check.py27 nan_to_num,
41 nan_to_num,
359 vals = nan_to_num(np.array((-1.0, 0, 1)) / 0.0)
366 vals = nan_to_num(np.array((-1.0, 0, 1)) / 0.0, nan=10, posinf=20, neginf=30)
372 vals = nan_to_num([1])
375 vals = nan_to_num([1], nan=10, posinf=20, neginf=30)
381 vals = nan_to_num(1)
384 vals = nan_to_num(1, nan=10, posinf=20, neginf=30)
390 vals = nan_to_num(1.0)
393 vals = nan_to_num(1.1, nan=10, posinf=20, neginf=30)
[all …]
/aosp_15_r20/external/pytorch/benchmarks/operator_benchmark/pt/
H A Dnan_to_num_test.py8 """Microbenchmarks for torch.nan_to_num / nan_to_num_ operators"""
10 # Configs for PT torch.nan_to_num / nan_to_num_ operators
15 ["nan_to_num", torch.nan_to_num],
44 self.set_module_name("nan_to_num")
/aosp_15_r20/external/pytorch/benchmarks/static_runtime/
H A Dtest_static_module.cc381 c = (a + b).relu().nan_to_num().float() in TEST()
382 d = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST()
392 c = (a + b).relu().nan_to_num().float() in TEST()
393 d = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST()
403 c = (a + b).relu().nan_to_num().float() in TEST()
405 e = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST()
415 c = (a + b).relu().nan_to_num().float() in TEST()
417 e = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST()
1728 z = y.nan_to_num() in TEST()
1736 EXPECT_FALSE(hasNodeWithKind(graph, "aten::nan_to_num")); in TEST()
[all …]
/aosp_15_r20/external/pytorch/test/
H A Dtest_unary_ufuncs.py473 lambda x: x.nan_to_num(nan=nan, posinf=posinf),
474 lambda x: np.nan_to_num(x, nan=nan, posinf=posinf),
478 lambda x: x.nan_to_num(posinf=posinf, neginf=neginf),
479 lambda x: np.nan_to_num(x, posinf=posinf, neginf=neginf),
485 result = torch.nan_to_num(x)
486 torch.nan_to_num(x, out=out)
489 result = torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf)
490 torch.nan_to_num(x, out=out, nan=nan, posinf=posinf, neginf=neginf)
508 return torch.nan_to_num
527 res = torch.nan_to_num(a, **{kwarg_name: 12})
[all …]
H A Dtest_tensorexpr_pybind.py335 return a.nan_to_num()
343 …%y : Float(2, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::nan_to_num(%x, %none, %none,…
357 kernel = te.TensorExprKernel(graph, {"aten::nan_to_num": my_custom_lowering})
/aosp_15_r20/external/pytorch/test/typing/pass/
H A Dmath_ops.py252 # nan_to_num
254 torch.nan_to_num(x)
255 torch.nan_to_num(x, nan=2.0)
256 torch.nan_to_num(x, nan=2.0, posinf=1.0)
/aosp_15_r20/external/pytorch/functorch/dim/
H A Dop_properties.py234 torch.Tensor.nan_to_num,
235 torch.nan_to_num,
/aosp_15_r20/external/pytorch/test/mobile/model_test/
H A Dmath_ops.py162 torch.nan_to_num(w),
164 torch.nan_to_num(w, nan=2.0),
165 torch.nan_to_num(w, nan=2.0, posinf=1.0),
H A Dcoverage.yaml423 - aten::nan_to_num
902 aten::nan_to_num: 3
/aosp_15_r20/external/pytorch/torch/ao/quantization/fx/_model_report/
H A Dmodel_report_observer.py212 quantile_ratios = torch.nan_to_num(quantile_ratios)
239 new_ratios = torch.nan_to_num(new_ratios)
/aosp_15_r20/external/pytorch/aten/src/ATen/native/sparse/
H A DSparseUnaryOps.cpp45 #include <ATen/ops/nan_to_num.h>
266 return at::nan_to_num(t, nan, posinf, neginf); in nan_to_num_sparse()
/aosp_15_r20/external/pytorch/torch/_numpy/
H A D_funcs_impl.py858 def nan_to_num( function
861 # work around RuntimeError: "nan_to_num" not implemented for 'ComplexDouble'
863 re = torch.nan_to_num(x.real, nan=nan, posinf=posinf, neginf=neginf)
864 im = torch.nan_to_num(x.imag, nan=nan, posinf=posinf, neginf=neginf)
867 return torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf)
/aosp_15_r20/external/pytorch/torch/onnx/
H A Dsymbolic_opset10.py44 "nan_to_num",
750 @_onnx_symbolic("aten::nan_to_num")
752 def nan_to_num(g: jit_utils.GraphContext, input, nan, posinf, neginf): function
/aosp_15_r20/external/pytorch/torch/distributed/tensor/_ops/
H A D_pointwise_ops.py309 aten.nan_to_num.default,
310 aten.nan_to_num.out,
/aosp_15_r20/external/pytorch/aten/src/ATen/native/mps/operations/
H A DTensorCompare.mm454 "nan_to_num: dtype of out: ",
477 string key = "nan_to_num" + getTensorsStringKey({self});
/aosp_15_r20/external/pytorch/aten/src/ATen/native/
H A DUnaryOps.cpp97 #include <ATen/ops/nan_to_num.h>
810 "nan_to_num: dtype of out: ", in nan_to_num_out()
826 Tensor nan_to_num( in nan_to_num() function
/aosp_15_r20/external/pytorch/torch/masked/maskedtensor/
H A Dunary.py54 "nan_to_num",
/aosp_15_r20/external/pytorch/functorch/op_analysis/
H A Dpublic_api100 nan_to_num
H A Dannotated_ops149 nan_to_num, composite pointwise
/aosp_15_r20/external/pytorch/aten/src/ATen/functorch/
H A DBatchRulesUnaryOps.cpp113 UNARY_POINTWISE_ALL(nan_to_num); in TORCH_LIBRARY_IMPL()
/aosp_15_r20/external/pytorch/docs/source/
H A Dmasked.rst121 nan_to_num
H A Dtorch.rst388 nan_to_num
/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/static/
H A Dpasses.cpp48 %y3 = aten::nan_to_num(%y2, %f, %g, %h) in ConcatAddMulReplaceNaNClip()
72 %y3 = aten::nan_to_num(%y2, %f, %g, %h) in ConcatAddMulReplaceNaNClip()
1400 %y : Tensor = aten::nan_to_num(%x, %nan, %posinf, %neginf) in FuseClampNaNToNum()
/aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/
H A DUnaryOpsKernel.cu228 AT_DISPATCH_COMPLEX_TYPES(iter.dtype(), "nan_to_num", [&]() { in nan_to_num_kernel_cuda()
/aosp_15_r20/external/pytorch/torch/_decomp/
H A D__init__.py370 aten.nan_to_num,

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